Project Title

segmentation_models.pytorch — A Python library for semantic segmentation models with 500+ pretrained backbones

Overview

segmentation_models.pytorch is a Python library that provides a collection of semantic segmentation models based on PyTorch. It offers a simple high-level API, 12 encoder-decoder model architectures, and over 800 pretrained convolutional and transformer-based encoders. The library is designed to be user-friendly, with a focus on ease of use and flexibility.

Key Features

Super simple high-level API for creating neural networks
12 different encoder-decoder model architectures, including Unet, Unet++, and Segformer
Over 800 pretrained convolutional and transformer-based encoders
Support for popular metrics and losses for training routines
ONNX export and torch script/trace/compile compatibility

Use Cases

Researchers and developers working on image segmentation tasks can use this library to leverage pretrained models and architectures.
The library can be used in applications such as medical image analysis, autonomous driving, and satellite imagery processing.
It can also be used for training custom models on specific datasets with a variety of encoders and architectures available.

Advantages

The library provides a wide range of pretrained models, making it easy to start with a strong baseline.
The simple API allows for quick prototyping and experimentation with different models and architectures.
The support for ONNX export enables easy integration with other tools and platforms.

Limitations / Considerations

The library is specifically designed for PyTorch, so users need to be familiar with this framework.
The performance of the models may vary depending on the specific use case and dataset, requiring fine-tuning and potentially additional data preprocessing.

MMSegmentation: An open-source semantic segmentation toolbox based on PyTorch, which offers a variety of models but with a different set of features and focus on modular design.
DeepLab2: A deep learning model for semantic image segmentation, originally developed by Google, which is known for its accuracy but differs in terms of supported architectures and ease of use.
TorchSeg: A PyTorch-based semantic segmentation library that provides a simple and unified interface for various models, differing in its approach to model organization and training流程.

Basic Information

GitHub: https://github.com/qubvel-org/segmentation_models.pytorch
Stars: 10,901
License: Unknown
Last Commit: 2025-09-18

📊 Project Information

Project Name: segmentation_models.pytorch
GitHub URL: https://github.com/qubvel-org/segmentation_models.pytorch
Programming Language: Python
⭐ Stars: 10,901
🍴 Forks: 1,781
📅 Created: 2019-03-01
🔄 Last Updated: 2025-09-18

🏷️ Project Topics

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📚 Documentation

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segmentation_models.pytorch

Project Description